Enormous amounts of data about organisms are being generated in the sequencing of genomes. Using this information to provide treatments and therapies for individuals will require an in-depth understanding of the gathered information. Efforts using genomic information have already led to the development of gene expression investigational devices. One of the most currently promising devices is the gene chip. Gene chips have arrays of oligonucleotide probes attached a solid base structure. Such devices are described in U.S. Pat. Nos. 5,837,832 and 5,143,854, herein incorporated by reference in their entirety. The oligonucleotide probes present on the chip can be used to determine whether a target nucleic acid has a nucleotide sequence identical to or different from a specific reference sequence. The array of probes comprise probes that are complementary to the reference sequence as well as probes that differ by one of more bases from the complementary probes.
The gene chips are capable of containing large arrays of oliogonucleotides on very small chips. A variety of methods for measuring hybridization intensity data to determine which probes are hybridizing is known in the art. Methods for detecting hybridization include fluorescent, radioactive, enzymatic, chemoluminescent, bioluminescent and other detection systems.
Older, but still usable, methods such as gel electrophosesis and hybridization to gel blots or dot blots are also useful for determining genetic sequence information. Capture and detection systems for solution hybridization and in situ hybridization methods are also used for determining information about a genome. Additionally, former and currently used methods for defining large parts of genomic sequences, such as chromosome walking and phage library establishment, are used to gain knowledge about genomes.
Large amounts of information regarding the sequence, regulation, activation, binding sites and internal coding signals can be generated by the methods known in the art. In fact, the amount of data being generated by such methods hinders the derivation of useful information. Human researchers, when aided by advanced learning tools such as neural networks can only derive crude models of the underlying processes represented in the large, feature-rich datasets.
Another area of biological investigation that can generate a huge amount of data is the emerging field of proteomics. Proteomics is the study of the group of proteins encoded and regulated by a genome. This field represents a new focus on analyzing proteins, regulation of protein levels and the relationship to gene regulation and expression. Understanding the normal or pathological state of the proteome of a person or a population provides information for the prognosis or diagnosis of disease, development of drug or genetic treatments, or enzyme replacement therapies. Current methods of studying the proteome involve 2-dimensional (2-D) gel electrophoresis of the proteins followed by analysis by mass spectrophotometry. A pattern of proteins at any particular time or stage in pathologenesis or treatment can be observed by 2-D gel electrophoresis. Problems arise in identifying the thousands of proteins that are found in cells that have been separated on the 2-D gels. The mass spectrophotometer is used to identify a protein isolated from the gel by identifying the amino acid sequence and comparing it to known sequence databases. Unfortunately, these methods require multiple steps to analyze a small portion of the proteome.
In recent years, technologies have been developed that can relate gene expression to protein production structure and function. Automated high-throughput analysis, nucleic acid analysis and bioinformatics technologies have aided in the ability to probe genomes and to link gene mutations and expression with disease predisposition and progression. The current analytical methods are limited in their abilities to manage the large amounts of data generated by these technologies.
One of the more recent advances in determining the functioning parameters of biological systems is the analysis of correlation of genomic information with protein functioning to elucidate the relationship between gene expression, protein function and interaction, and disease states or progression. Genomic activation or expression does not always mean direct changes in protein production levels or activity. Alternative processing of mRNA or post-transcriptional or post-translational regulatory mechanisms may cause the activity of one gene to result in multiple proteins, all of which are slightly different with different migration patterns and biological activities. The human genome potentially contains 30,000 genes but the human proteome is believed to be 50 to 100 times larger. Currently, there are no methods, systems or devices for adequately analyzing the data generated by such biological investigations into the genome and proteome.
Knowledge discovery is the most desirable end product of data collection. Recent advancements in database technology have lead to an explosive growth in systems and methods for generating, collecting and storing vast amounts of data. While database technology enables efficient collection and storage of large data sets, the challenge of facilitating human comprehension of the information in this data is growing ever more difficult. With many existing techniques the problem has become unapproachable. Thus, there remains a need for a new generation of automated knowledge discovery tools.
As a specific example, the Human Genome Project is populating a multi-gigabyte database describing the human genetic code. Before this mapping of the human genome is complete, the size of the database is expected to grow significantly. The vast amount of data in such a database overwhelms traditional tools for data analysis, such as spreadsheets and ad hoc queries. Traditional methods of data analysis may be used to create informative reports from data, but do not have the ability to intelligently and automatically assist humans in analyzing and finding patterns of useful knowledge in vast amounts of data. Likewise, using traditionally accepted reference ranges and standards for interpretation, it is often impossible for humans to identify patterns of useful knowledge even with very small amounts of data.
In recent years, machine-learning approaches for data analysis have been widely explored for recognizing patterns which, in turn, allow extraction of significant information contained within a large data set which may also include data that provide nothing more than irrelevant detail. Learning machines comprise algorithms that may be trained to generalize using data with known outcomes. Trained learning machine algorithms may then be applied to predict the outcome in cases of unknown outcome. Machine-learning approaches, which include neural networks, hidden Markov models, belief networks and support vector machines, are ideally suited for domains characterized by the existence of large amounts of data, noisy patterns and the absence of general theories.
The majority of learning machines that have been investigated are neural networks trained using back-propagation, a gradient-based method in which errors in classification of training data are propagated backwards through the network to adjust the bias weights of the network elements until the mean squared error is minimized. A significant drawback of back-propagation neural networks is that the empirical risk function may have many local minimums, a case that can easily obscure the optimal solution from discovery. Standard optimization procedures employed by back-propagation neural networks may converge to a minimum, but the neural network method cannot guarantee that even a localized minimum is attained, much less the desired global minimum. The quality of the solution obtained from a neural network depends on many factors. In particular, the skill of the practitioner implementing the neural network determines the ultimate benefit, but even factors as seemingly benign as the random selection of initial weights can lead to poor results. Furthermore, the convergence of the gradient-based method used in neural network learning is inherently slow. A further drawback is that the sigmoid function has a scaling factor, which affects the quality of approximation. Possibly the largest limiting factor of neural networks as related to knowledge discovery is the “curse of dimensionality” associated with the disproportionate growth in required computational time and power for each additional feature or dimension in the training data.
The shortcomings of neural networks are overcome using support vector machines. In general terms, a support vector machine maps input vectors into high dimensional feature space through non-linear mapping function, chosen a priori. In this high dimensional feature space, an optimal separating hyperplane is constructed. The optimal hyperplane is then used to determine things such as class separations, regression fit, or accuracy in density estimation.
Within a support vector machine, the dimensionally of the feature space may be huge. For example, a fourth degree polynomial mapping function causes a 200 dimensional input space to be mapped into a 1.6 billionth dimensional feature space. The kernel trick and the Vapnik-Chervonenkis (“VC”) dimension allow the support vector machine to thwart the “curse of dimensionality” limiting other methods and effectively derive generalizable answers from this very high dimensional feature space.
If the training vectors are separated by the optimal hyperplane (or generalized optimal hyperplane), then the expectation value of the probability of committing an error on a test example is bounded by the examples in the training set. This bound depends neither on the dimensionality of the feature space, nor on the norm of the vector of coefficients, nor on the bound of the number of the input vectors. Therefore, if the optimal hyperplane can be constructed from a small number of support vectors relative to the training set size, the generalization ability will be high, even in infinite dimensional space. Support vector machines are disclosed in U.S. Pat. Nos. 6,128,608 and 6,157,921, both of which are assigned to the assignee of the present application and are incorporated herein by reference.
The data generated from genomic and proteomic tests can be analyzed from many different viewpoints. The literature shows simple approaches such as studies of gene clusters discovered by unsupervised learning techniques (Alon, 1999). For example, each experiment may correspond to one patient carrying or not carrying a specific disease (see e.g. (Golub, 1999)). In this case, clustering usually groups patients with similar clinical records. Supervised learning has also been applied to the classification of proteins (Brown, 2000) and to cancer classification (Golub, 1999).
Support vector machines provide a desirable solution for the problem of discovering knowledge from vast amounts of input data. However, the ability of a support vector machine to discover knowledge from a data set is limited in proportion to the information included within the training data set. Accordingly, there exists a need for a system and method for pre-processing data so as to augment the training data to maximize the knowledge discovery by the support vector machine.
Furthermore, the raw output from a support vector machine may not fully disclose the knowledge in the most readily interpretable form. Thus, there further remains a need for a system and method for post-processing data output from a support vector machine in order to maximize the value of the information delivered for human or further automated processing.
In addition, the ability of a support vector machine to discover knowledge from data is limited by the type of kernel selected. Accordingly, there remains a need for an improved system and method for selecting and/or creating an appropriate kernel for a support vector machine.
Further, methods, systems and devices are needed to manipulate the information contained in the databases generated by investigations of proteomics and genomics. Also, methods, systems and devices are needed to integrate information from genomic, proteomic and traditional sources of biological information. Such information is needed for the diagnosis and prognosis of diseases and other changes in biological and other systems.
Furthermore, methods and compositions are needed for treating the diseases and other changes in biological systems that are identified by the support vector machine. Once patterns or the relationships between the data are identified by the support vector machines of the present invention and are used to detect or diagnose a particular disease state, diagnostic tests, including gene chips and tests of bodily fluids or bodily changes, and methods and compositions for treating the condition are needed.